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learn mlops & production ml
take a model from notebook to production: tracking, serving, monitoring, and the systems thinking around it.
the curated path
curatedintermediate~5 weeks, part-time
mlops & production ml
the gap between a notebook and a system people rely on. learn to track, serve, monitor, and maintain models — the engineering that makes ml real.
4 modules · 12 resources · checkpoint per modulestay current
what's new in mlops & production ml
- Operational AI Deployment Assurance: Governance-State Orchestration Under Threshold-Sensitive Deployment Conditionsproposes a governance framework for deploying high-stakes ai systems that orchestrates deployment decisions based on fairness, transparency, and risk thresholds. teams building models for regulated domains or critical applications need this framework to ensure deployments meet governance requirements before going live.
- TimeGate: Sustainable Time-Boxed Promotion Gates for Continual ML Adaptation Under Resource Constraintsintroduces a policy layer that manages retraining budgets—compute, annotation, energy—for continuously adapting ml systems, preventing runaway costs in production. practitioners operating models that drift over time can use this approach to make principled decisions about when and how often to retrain.
- The role of explainability throughout the MLOps lifecycle: review and research agendareviews how explainability should be embedded across all mlops stages—not just as a post-hoc audit tool—to support debugging, monitoring, and stakeholder trust. practitioners will learn where to prioritize interpretability investments and how to integrate explanation techniques into continuous deployment workflows.
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